The January 2019 DB-Engines newsletter compiles the newest releases, feature highlights, and performance improvements across RDBMS, NoSQL, NewSQL, time‑series, big‑data, domestic, and cloud database families, while also explaining the ranking methodology and providing download links for the full issue.
This article describes Ctrip's call‑center staffing challenge, detailing data cleaning, trend analysis, feature engineering, the initial ARIMAX‑Fourier model (V1.0), its limitations, and the improved V2.0 solution that combines TBATS, ARIMA residuals and XGBoost, achieving up to 89.5% prediction accuracy.
This article explains how to process a periodic time‑series, split it into training and test sets, smooth the data, decompose it with statsmodels' seasonal_decompose, forecast the trend using an ARIMA model, and evaluate the results with RMSE, providing a practical workflow for accurate forecasting.
The article explains how a data analyst can use statistical and machine‑learning models such as linear regression, tree‑based boosting, STL decomposition, and Prophet for both non‑time‑series forecasting and time‑series monitoring, highlighting data‑quality concerns, feature‑engineering practices, and deployment considerations like PMML packaging.
This article explains how 360’s network operations team uses time‑series analysis, statistical thresholds, EWMA, dynamic limits, and machine‑learning models such as K‑Means and Isolation Forest to automatically detect, locate, and remediate traffic anomalies across massive data‑center exits.
The article recounts the author’s 2017‑onward journey building an intelligent operations platform at Tencent, detailing challenges such as legacy thresholds, AIOps talent shortage, and lack of frameworks, and explains how a two‑stage hybrid unsupervised‑supervised model was devised to automatically detect anomalies across millions of KPI time‑series, enabling scalable root‑cause analysis and cost optimization.
This article compares Grafana and Kibana/Timelion, explaining their data source support, query syntax, visualization capabilities, and user interaction features to help developers choose the most suitable tool for real‑time time‑series monitoring and analysis.
A 2017 roundup highlights ten innovative database releases—including a time‑series extension for PostgreSQL, a multi‑model Azure service, Google’s globally distributed Spanner, Amazon’s Neptune graph service, and several open‑source cloud‑native databases—detailing their key features, architectures, and intended use cases.
This article introduces a practical LVS traffic anomaly detection method that combines short‑term and long‑term ratio analyses, dynamic thresholds, and periodicity‑aware techniques, providing code examples and a decision flow to help ops teams identify sudden traffic spikes or drops accurately.
The October 2017 DBAplus Newsletter summarizes the newest releases across RDBMS, NoSQL, NewSQL, time‑series, big‑data and domestic databases, highlighting key features, performance improvements, and ecosystem updates while providing links to official documentation and source repositories.
Redis introduced the Streams data type to address limitations of sorted sets, lists, and Pub/Sub for handling continuous data flows, offering features like field-value entries, efficient range queries, and client blocking with ID tracking, and the article explains its design, commands, and usage examples.
This article walks through the fundamentals of InfluxDB as a time‑series database, demonstrates how to write data via its HTTP API, and shows how to configure Grafana for real‑time visualization, high‑availability deployment, and alerting in a big‑data monitoring scenario.
This article details Alibaba's middleware team's exploration of time‑series data characteristics, real‑world monitoring scenarios, the limitations of traditional databases, and the evolution of their custom HiTSDB solution that combines inverted indexing, high‑compression algorithms, and distributed aggregation to meet massive write and query demands.
This article presents the motivation, architecture, functional and interface layers, storage design, and performance evaluation of CFL, a MySQL plug‑in storage engine created by Ctrip to provide a high‑throughput, SQL‑based time‑series database while preserving existing MySQL operational practices.
This article presents the design, architecture, and performance evaluation of CFL, a MySQL time‑series storage engine developed by Ctrip, detailing its functional and interface layers, storage strategy, and benchmark results compared with InnoDB, MyISAM, and other engines.
This article details Ctrip's motivation, design, implementation, performance evaluation, and future plans for a custom MySQL storage engine called CFL that efficiently stores time‑series data by leveraging MySQL's replication and a sequential write‑optimized file format.
Time‑series data, defined by a timestamp field, appears everywhere, and the article explains how to choose an appropriate time‑series database by comparing two schema models, their query patterns, performance trade‑offs, and why modern solutions like Elasticsearch, columnar stores, and Druid excel at real‑time massive aggregation.
This article examines the evolution and challenges of time‑series storage, compares file‑based, LSM‑tree, and B‑tree approaches, and proposes a vector‑based method to efficiently handle writes, reads, query semantics, dimensions, and aggregation for modern big‑data applications.
